meraGPT - Small models for one job; Decider 1 leads typed-decisions

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Small models that each do one job. Decider 1 answers typed questions about any state as probabilities and is #1 on typed-decisions, Hugging Face's decision benchmark. Restyler 1 makes model output read human. Query Fanout 1 predicts AI searches.

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Hey Product Hunt 👋 meraGPT is an API for small models that each do one job. Restyler 1 takes text from any model and rewrites it so it reads the way a person writes. We measured it with a classifier trained to tell model output from human writing on the same prompts, where 0.5 means it can't tell and 1.0 means it's obvious. Claude Haiku 4.5, Gemini 3.5 Flash Lite and GPT-5.6 Luna all score about 0.90 on their own. After Restyler 1 the same text scores 0.65 to 0.70. Gemini was held out of training entirely and still improved the most. It keeps 73% of facts on average, and every fact in only 41% of blocks, so read the output wherever accuracy matters. It was not built or measured for getting past AI detectors. Query Fanout 1 takes a question and returns the searches an assistant would likely run before answering. It learned from searches we observed ChatGPT, Claude and Gemini actually make. If you care whether your pages get cited in AI answers, those searches are what decide it. Both are OpenAI-compatible and cost $0.05 per million input tokens and $0.15 per million output. The playgrounds work without an account. We'd love to hear what you would point them at, and which narrow job you'd want a model for next.

Since we scheduled this launch, a third model joined: Decider 1. You send it a piece of state (a support conversation, an alert, an invoice, an agent trace) and typed questions about it: yes or no, a choice between labels, or a score on a rubric. Each answer comes back as a probability distribution, and twenty questions over one state are answered in one pass. It is state of the art on typed-decisions, the official Hugging Face benchmark for decision models: 0.768 accuracy and a Brier score of 0.052, against 0.727 and 0.148 for TypeSafe Jev 1.13. It costs $0.04 per million input tokens, output is free, and new accounts get $1 of credit.